scholarly journals An Electrical Energy Consumption Monitoring and Forecasting System

2016 ◽  
Vol 6 (5) ◽  
pp. 1130-1132
Author(s):  
J. L. Rojas-Renteria ◽  
T. D. Espinoza-Huerta ◽  
F. S. Tovar-Pacheco ◽  
J. L. Gonzalez-Perez ◽  
R. Lozano-Dorantes
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Grid Technology ◽  
Electrical Energy Consumption ◽  
Accurate Forecast ◽  
Monitoring And Forecasting ◽  
Forecasting System ◽  
Smart Grid Technology

Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

scholarly journals Conceptual Framework of Modelling for Malaysian Household Electrical Energy Consumption using Artificial Neural Network based on Techno-Socio Economic Approach

2018 ◽  
Vol 8 (3) ◽  
pp. 1844 ◽  
Author(s):  
Boni Sena ◽  
Sheikh Ahmad Zaki ◽  
Fitri Yakub ◽  
Nelidya Md Yusoff ◽  
Mohammad Kholid Ridwan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Quantitative Measurement ◽  
Electrical Energy ◽  
Qualitative Assessment ◽  
Tropical Region ◽  
Economic Factors ◽  
Electrical Energy Consumption ◽  
Socio Economic Factors

The residential sector was one of the contributors to the increase in the world energy consumption and CO2 emission due to the increase population, economic development, and improved living standard. Developing a reliable model of electrical energy consumption based on techno-socio economic factors was challenging since many assumptions need to be considered. Over the past decade, bottom-up approaches such as multi-linear regression, artificial neural network (ANN), and conditional demand analysis were used for developing mathematical models to investigate interrelated characteristics among techno-socio economic factors. However, the existing models mostly were focused on countries that had different socio-economic level and cultures from the developing countries of the Association of Southeast Asian Nations. Similar studies in that tropical region were very scarce and only limited for linear modelling under the conditions of techno-socio economic factors. In this study, we proposed ANN for developing a model of electrical energy consumption based on techno-socio economic factors for a tropical region, Malaysia. In order to develop the model, quantitative measurement and qualitative assessment were required. The quantitative measurement was based on the monitoring of total electrical energy consumption with a one-minute interval. In contrast, the qualitative assessment utilized a questionnaire survey to assess household characteristics based on techno-socio economic parameters. The objective of this paper was to propose a conceptual framework of the estimation model for household electrical energy consumption with the consideration of techno-socio economic factors using ANN.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

2017 ◽  
Vol 8 (1) ◽  
pp. 137
Author(s):  
Wan n Nazirah Wan Md Adna ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Use ◽  
Model Development ◽  
Operating Time ◽  
Electrical Energy ◽  
Measurement And Verification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hybrid Artificial Neural Network

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2018 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

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